Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes

被引:5
|
作者
Buj-Corral, Irene [1 ]
Sivatte-Adroer, Maurici [2 ]
机构
[1] Univ Politecn Catalunya UPC, Dept Mech Engn, Barcelona Sch Ind Engn ETSEIB, Ave Diagonal 647, Barcelona 08028, Spain
[2] Univ Politecn Catalunya UPC, Sch Engn Vilanova & Geltru EPSEVG, Dept Mech Engn, Ave Victor Balaguer 1, Vilanova I La Geltru 08800, Spain
关键词
honing; material removal rate; tool wear; regression models; multi-objective optimization; SURFACE-ROUGHNESS; PARAMETERS; SIZE;
D O I
10.3390/machines10020083
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min(-1). The other variables, density and pressure, would not change significantly from the first situation.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems
    Buj-Corral, Irene
    Sender, Piotr
    Luis-Perez, Carmelo J.
    [J]. TRIBOLOGY INTERNATIONAL, 2023, 182
  • [2] Modelling of surface finish and material removal rate in rough honing
    Buj-Corral, Irene
    Vivancos-Calvet, Joan
    Coba-Salcedo, Milton
    [J]. PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2014, 38 (01): : 100 - 108
  • [3] Multi-Objective Tool Sequence and Parameter Optimization for Rough Milling Applications
    Churchill, Alexander W.
    Husbands, Phil
    Philippides, Andrew
    [J]. 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2013, : 1475 - 1482
  • [4] Multi-Objective Optimization of Machining Parameters Based on Tool Wear Condition
    Tian, Ying
    Wang, Wenhao
    Yang, Liming
    Shao, Wenting
    [J]. Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology, 2022, 55 (02): : 166 - 173
  • [5] Multi-objective optimization of material removal rate and surface roughness in wire electrical discharge turning
    Krishnan, S. Aravind
    Samuel, G. L.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2013, 67 (9-12): : 2021 - 2032
  • [6] Multi-objective optimization of material removal rate and surface roughness in wire electrical discharge turning
    S. Aravind Krishnan
    G. L. Samuel
    [J]. The International Journal of Advanced Manufacturing Technology, 2013, 67 : 2021 - 2032
  • [7] Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear
    Bo Li
    Xitian Tian
    Min Zhang
    [J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 2022, 9 : 127 - 141
  • [8] Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear
    Li, Bo
    Tian, Xitian
    Zhang, Min
    [J]. INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY, 2022, 9 (01) : 127 - 141
  • [9] Surface Roughness Prediction and Multi-Objective Optimization of Honing Cylinder Liner
    Lü, Yanjun
    Li, Jie
    Jiang, Cheng
    Li, Pengzhou
    Zhang, Yongfang
    Chang, Huan
    [J]. Mocaxue Xuebao/Tribology, 2022, 42 (04): : 728 - 741
  • [10] Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools
    Meng, Yue
    Dong, Shengming
    Sun, Xinsheng
    Wei, Shiliang
    Liu, Xianli
    [J]. COATINGS, 2024, 14 (06)